Well-being and happiness, individually and collectively, is a main
indicator of a good life. This paper attempts to implement empirically
some of the multidimensional concepts of human well-being by using data
from the 'Pakistan Living Standards Measurement Survey'
2006-07. Objective well-being index and subjective well-being index are
constructed to study regional disparities in the quality of life. The
results reveal that most of the top ranked districts are located in the
province of Punjab which tends to indicate that Punjab is ahead of other
provinces in terms of objective well-being. Sindh and NWFP districts are
dominated in the category of lower medium well-being category. At the
lower end of the distribution districts of Balochistan emerged in lowest
category of well-being. It is observed that Punjab have highest share of
population in top category of well-being while population of Balochistan
gets major share in lowest category of well-being. It is important to
note that those districts which have higher achievements in hard facts
of well-being, acquire less subjective well-being in term of
satisfaction. Districts of Balochistan, with least developed indicators,
perception about the quality of life is evident in their lowest level of
satisfaction. Since the underlying premise of the MDGs is still the
concept of human development, so priorities is needed to concentrate on
least developed districts for achieving the MDGs by 2015.

The concept of well-being has deep roots in philosophy [Cantril
(1965)]. Much later in the 19th century modern definitions of well-being
emerged. The utilitarian movement defined well-being subjectively and
proclaimed individuals' well-being as an important goal of
individuals' behaviour and public policy. During the 20th century
social scientists started to examine well-being empirically, but a
unified concept of well-being was lacking. At the beginning of the 20th
century, economists developed elaborate quantitative theories of
well-being, but rejected the possibility that individuals' could
provide valid reports of their own well-being. In the second half of the
20th century social scientists started to develop subjective measures of
well-being, and started to examine how these measures relate to
demographic variables or other characteristics of individuals [Andrews
and Withey (1976)].

The relationship between GDP and well-being likely depends on how
rich a country is. As income increases it contributes little to overall
well-being at low levels of GDP in poor country, since only a narrow
segment of the population is benefiting directly. Moreover, as noted by
Sen (2001) non-monetary benefits such as health and education that
improve individual capabilities are often more important than income in
poor countries. As the benefits of continued growth trickle down to a
burgeoning middle class, social well-being rises dramatically [Torras
(2008)]. It is in this context that a number of alternatives to GDP have
been introduced. For example, the United Nations Development
Programme's (UNDP) human development index (HDI) uses GDP per
capita to measure "access to economic resources" in well-being
assessments but accords it only one-third weight in determination of the
level of human development. Although national income accounting measures
may sometimes not agree with popular perceptions of trends in economic
well-being, GDP per capita is one of the three main components of the
HDI, whose objective is to indicate the capability of people "to
lead a long and healthy life, to acquire knowledge and to have access to
resources needed for a decent standard of living" [Osberg and
Andrew (2005)]. A second approach, multi-criteria analysis, is the Human
Well-being Index which measures more realistically socioeconomic
conditions than narrowly monetary indicators such as the GDP and covers
more aspects of human well-being than HDI. 'Human Well-being is a
condition in which all members of society are able to determine and meet
their needs and have a large range of choices and opportunities to
fulfil their potential' that generates a more comprehensive picture
of the state of the world. It is the average of indices of health and
population, wealth, knowledge, community and equity [Prescott-Allen
(2003)].

The principal thrust of human well-being has been to supplement
traditional economic indices of well-being with alternative indicators
that capture non-economic or non-material dimensions of human life. In
particular, it is now commonly accepted that human well-being should be
treated as a multidimensional concept along the lines advocated by Sen
(1993). He emphasised on promotion of human well-being and development
by adding another dimension of well-being research. He argued that
quality of life do not depend merely on opportunities and is determined
by human capabilities as well. Classifying various well-being
definitions, distinction between objective and subjective definitions of
well-being is important which is based on the selection process of the
criteria that are used to judge individuals' well-being. Objective
definitions assume that the criteria can be defined without reference to
the individual's own preferences, interests, ideals, values, and
attitudes. The objective indicators of well-being are only proxies;
these are indirect measures of true conditions that researchers try to
evaluate. It is assumed that the objective circumstances influence
satisfaction within specific life domain [Sumner (1996)]. Objective
measurement is based on explicit criteria and performed by external
observer. Subjective definitions require that individual preferences,
interests, ideals, values, and attitudes matter. Well-being indicators
can also be subjective which is based on people's perceptions of
their happiness and satisfaction with living standards. These indicators
are survey based and directly enquire individuals about their
satisfaction with life [Hasan (2008)]. Subjective measurement involves
self reports based on implicit criteria.

In response to the changing global conditions, new research
priorities and improved data resources, social science research on
living standards, human well-being and quality of life has altered. In
this scenario all United Nations Member States in 2000, adopted the
eight Millennium Development Goals (MDGs) as a framework for the
development activities of over 190 countries in ten regions; they have
been articulated into over 20 targets and over 60 indicators, towards
the target date--2015--by which the MGDs are to be achieved. Pakistan
has adopted 16. targets and 37 indicators for monitoring the MDGs. Since
then the Millennium Development Goals have become a universal framework
for development and a means for developing countries and their
development partners to work together in pursuit of a shared future for
all. The underlying premise of the MDGs is still the concept of human
development. It is noted that the MDGs concentrate on the non-monetary
variables which are not measured in terms of monetary units; rather the
goals focus on the distribution of capabilities-education, health,
nutrition, gender relations, and physical environment. They are
characterised as qualitative variables or in terms of quantity [United
Nation (2002)].

This paper proposes a conception of dimensions of human well-being:
objective well-being by concentrating on MDGs, i.e., education, health
and environmental sustainability to determine the extent of variation
among districts of Pakistan in the level of well-being. It also focuses
on softer issues of subjective well-being, i.e., satisfaction with
facilities/services used, education, health and security. It also
elaborates a basic configuration of objective and subjective well-being
across districts of Pakistan.

The notion of well-being is receiving growing attention, both in
academic research and policy-oriented analysis, especially in the
context of MDGs. There is expanding literature that provides various
measures of well-being which are discussed here.

Schimmack (2008) defined well-being as preference realisation which
can be measured with affective and cognitive measures. The paper
examined similarities and differences between cognitive measures of
well-being and four items (happy, sad, angry, and afraid) as an
affective measure of well-being.

Prescott-Allen (2003) prepared a common framework of dimensions
consisting of (a) human dimensions, including health and population,
national and household wealth, education and culture, community and
social capital, and equity; and (b) ecosystem dimensions, including land
and forests, water quality and diversity, air quality, species and
genetic diversity, and energy and resources use.

Sumner (1996) provided distinction between objective and subjective
definitions of well-being. The distinction is based on the selection
process of the criteria that are used to judge individuals'
well-being. Objective definitions assume that the criteria can be
defined without reference to the individual's own preferences,
interests, ideals, values, and attitudes while subjective definitions
require that individuals' preferences, interests, ideals, values,
and attitudes matter.

Hasan (2008) explored the concepts of city ranking as a way to
measure the dynamics and complexities of urban quality of life. These
ranking had various dimensions and uses. Both the context in which these
rankings were organised and their nature had changed considerably over
time.

Akhtar and Sarwer (2007) employed two different techniques-Z sum
and weighted factor scores and 12 indicators to quantify the
intertemporally compared levels of development in the districts of
Pakistan. The study highlighted that provincial capital, i.e., Karachi,
Lahore and Quetta consistently appear in the top ten ranking under both
techniques in 1998 and 2005. In regressive districts, 5 belonged to
Balochistan, 3 from Punjab and two districts were found from Sindh
province.

Jamal and Amir (2007) highlighted changes in human development
status in districts of Pakistan during the period 1998 and 2005. The
estimates of a district level Human Development Indices provide an
indication of existing trends in regional disparities in terms of
economic development as well as education and health status.

Uddin (2007) reviewed social development in Pakistan with focus on
the issues of access to and quality of social services and identified
areas that should receive greater attention to enhance the public access
to quality social services. It was observed that the demand for social
services is expanding rapidly, mainly owing to high population growth
and rapid urbanisation.

Siddiqui (2006) tested whether direct provision of social services
improve capabilities by estimating a basic need model for Pakistan. She
viewed that government provision of social services affects human
capabilities significantly. She analysed that aggregate statistics at
the national or provincial level hides region specific reason of poverty
and inequalities. The variations in these indicators across the district
within a province and across the provinces are an indicative of regional
disparities in terms of income, health, education and the quality of
life.

UNDP (2003) estimated that variation in Human Development Indices
between provinces and districts are indicative of regional disparities
in both the level of economic growth as well as in terms of health,
education and quality of life.

Midhet (2004) derived development ranking by applying composite
indices of several district-level variables derived from factor
analysis, which are then used to predict two important indicators of
reproductive health; the child-woman ratio(CWR) and maternal mortality
rate (MMR).This study was designed to facilitate selection of districts
for implementing operations research in safe motherhood. It is indicated
that MMR decreased with accessibility of hospitals and primary health
facilities. The study also identified which districts are developing
satisfactorily and which are stagnant or deterioration in terms of
development.

Pasha and Naeem (1999) examined whether the low level of social
indicators in the country is a consequence of poor initial conditions or
has there been deterioration due to relatively low rate of improvement
over time? The study concluded that Pakistan is a case of a country
which not only started with low level of human endowment but the
situation has been exacerbated by the low level of improvement in it
over time.

Ghaus, et al. (1996) explored regional variation in the development
of social infrastructure across districts of Pakistan. The study
demonstrated the importance of education indicators in determining the
overall level of social development in terms of female literacy and
enrolment rates. However the analysis indicated substantial variation
among districts within a province in the level of social development.
Least developed districts within each province are identified as targets
for special development.

Pasha, et al. (1990) demonstrated that there are marked changes in
the development ranking of a number of districts from the early
1970's to the early 1980's, especially among districts at the
intermediate level of development. The indicators were selected from
diverse sectors like industry, agriculture, transport and communications
with basic social indicators including education, health, gender
equality and housing. Districts of Punjab have generally improved their
ranking in the education sector, gender equality and labour force
indicators while province of Balochistan continued to fall behind the
rest of the country.

Jamal and Salman (1988) concluded that despite the regional
development policies pursued in the province of Sindh during the 70s
little success has been achieved in narrowing regional disparities among
districts. It is indicated that there is need for a fundamental
re-evaluation of nature, scope and content of these policies.

Pasha and Tariq (1982) indicated that districts development
rankings hide major intraprovincial disparities. The analysis
demonstrates that all the provincial capitals and federal capital are
included in top quartile of the national population. Provinces that are
considered relatively underdeveloped like Balochistan and NWFP to have
some highly developed pockets while a significant part of Punjab and
Sindh appeared to be relatively underdeveloped.

The above studies discussed various measures of well-being and
districts level social development in Pakistan. It is concluded that
there is substantial variation among districts within a province in the
level of social development and districts of Balochistan are identified
as least developed in terms of quality of life.

3. DATA AND METHODOLOGY

3.1. Data

The study employs the 'Pakistan Social and Living Standards
Measurement Survey' (PSLM) 2006-07 data which consists of Core
Welfare Indicators Questionnaire (CWIQ) approach. It is one of the main
mechanisms for monitoring the implementation of the MDGs and Poverty
Reduction Strategy Paper (PRSP). It provides a set of representative,
population-based estimates of social indicators and their progress under
MDGs and PRSP. An important objective of the PSLM Survey is to try to
establish what is the distributional impact of different government
programs carried out in social sector. Policymakers need to know, for
example, whether the poor have benefited from the programme or whether
increased government expenditure on the social sectors has been captured
by the better off. PSLM Survey consists of data relating education,
child health, maternal health, household assets/amenities. It also
provides subjective data relating to perception of economic situation of
the households and communities where they live and satisfaction of
services. The sample size for the four provinces has been fixed at 73953
households comprising 5198 sample villages / enumeration blocks, which
is expected to produce reliable results at each district [Pakistan
(2008)].

3.2. Methodological Choices Encountered in the Construction of
Composite Indices of Well-being

The first choice encountered in index construction is the general
form of the index: will it be a single composite, or a complementary
composite. A single composite is a single aggregation of variables that
are used in an index, whereas a complementary composite is comprised of
two separate indices: a conglomerative index and a deprivational index.
A conglomerative index measures the overall well-being of a society, in
contrast, a deprivational index measures only the welfare of the worst
off.

The next choice encountered is which variables to include in the
index. This choice can be made by simply choosing data that an index
constructor wants to include, or by first determining concepts that the
developers seek to measure, such as inequality. After variables have
been picked, functional forms must be chosen. The functional form is a
functional transformation that is applied to the raw data in order to
represent the significance of marginal changes in its level. Once
functional forms associated to variables have been established, a
uniform method of standardisation should be considered. One choice is to
use raw data and not standardise. This choice leads to many problems
when an attempt is made to aggregate variables. Standardisation methods
allow standardised values to be compared meaningfully. Three techniques
to standardise absolute values of variables are reviewed: Linear Scaling
Technique which linearly scales variables to a uniform range, ordinal
response, where experts assign a score to each variable, and Gaussian
normalisation, or Z-score, in which the standardised variable is the
number of standard deviations away from its mean.

The final step in forming a composite index is setting the weights
within the aggregation scheme. The most widely accepted and used
techniques to set explicit weights in aggregation are: expert weighting
set by specialist, Principal Component Analysis and explicitly set
weights by another mechanism, such as equal weighting [Salzman (2003)].

3.3. Strategies to Study Dimensions of Well-being

The multidimensional view of well-being is receiving growing
attention, both in academic research and policy-oriented analysis. The
multifaceted nature of well-being is implicit in the set of indicators
to monitor the performance of countries. Indicators are commonly
recommended as tools for assessing the attainment of development, and
the current vogue is for aggregating a number of indicators together
into a single index. It is claimed that such indices of development help
to facilitate maximum impact in policy terms by appealing to those who
may not necessarily have technical expertise in data collection,
analysis and interpretation. This paper constructs indices of well-being
by focusing on the (UNDP) Human Development Index (HDI). While the HDI
offers a composite index that summarises basic choices available to
people, it has been criticised on many grounds. For example, it is
argued that it does not capture the totality of issues that affect human
well-being. Hence, this study is being made to widen the scope of issues
covered by the index. The study examines the non-income dimensions of
objective well-being that contribute to quality of life, i.e.,
education, child health, maternal health and housing facilities that
affect human well-being while their absence will constitute some form of
deprivation. Subjective well-being index is also developed to measure
individuals' preferences, interests, ideas, values, and attitudes
towards the satisfaction of facilities available, i.e. education, health
and security. After selecting the variables 'Linear Scaling
Technique' which linearly scales variables to a uniform range is
applied before aggregating. However, for ease of comparison, this index
is standardised to a scale of 0 to 1.

(a) Linear Scaling Technique (LST)

Let [X.sub.1], [X.sub.2] ..., [X.sub.n] be the indicators. The
indicators are standardised to maintain uniformity. Each of the
[X.sub.i]'s are observed for each district.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

X[min.sub.ij] = Minimum value of ith indicaor in jth district

[X.sub.ij] = Value of ith indicator in jth district

X[max.sub.ij] = Maximum value of ith indicaor in jth district

3.4. Dimensions of Objective Well-being Index (OWBI)

Dimensions of well-being are non-hierarchical, irreducible,
incommensurable and hence basic kinds of human ends. Objective
well-being assumes that the criteria can be defined without reference to
the individual's own preferences, interests, ideas, values, and
attitudes. Its indicators are based on attributes that can be measured,
for example maternal mortality rate, poverty rates and adult literacy
rate, etc. In this study three basic components education, health and
living conditions with sub components are taken to rank districts on the
basis of objective well-being followed by [Akhtar and Sarwer (2007)]. It
is assumed that the selected objective indicators of well-being are only
proxies, i.e., they are indirect measures of true conditions of
well-being that also influence satisfaction with specific life domain.
In this study a non monetary well-being index is preferred to explain
the group of variables with equal weights for each of its domain.

The formula for the overall index comprises of three main
components (education, health and living conditions) each affecting, in
one way or another, a human being's life by way of his / her
success to 'means' or desires 'ends'. Let [X.sub.1],
[X.sub.2] ..., [X.sub.n] be the indicators. The indicators are
standardised by 'Linear Scaling Technique' to maintain
uniformity. Each of the [X.sub.i]'s are observed for each district.

[[SFI.sub.j]]=Source of lighting index, [[SFI.sub.j]]=Source of
fuel for cooking index.

A summary of objective well-being indicators are given in Table 1
with values of minimum, maximum, mean, coefficient variation and MDGs
targets. The variation in these indicators of well-being across the
districts of Pakistan is an indicative of regional disparities in the
quality of life.

3.5. Choice of Indicators

To measure objective well-being three goals of MDGs are taken, i.e,
education, health and environmental sustainability.

(i) Education

Goal 2: Universal Primary Education.

Goal 3: Promote Gender Equality and Empower Women.

MDGs Goal 2 aims at ensuring that by 2015 children everywhere, boys
and girls alike would be able to complete a full course of primary
schooling. This target is assessed in Pakistan by the trends in gross
and net enrolments, the proportion of students who completed their
studies from grade one to grade five and adult literacy rates. In
this-study two indicators are taken to analyse universal primary
education; literacy, net enrolment at primary level. Literacy is taken
as the ability to read a newspaper and to write a simple letter.
Population aged 10 years and older that is literate expressed as a
percentage of the population age 10 years and older. Net enrolment rate
at primary level is taken as [number of children age 5-9 years attending
primary level (classes 1-5) divided by number of children aged 5-9
years] multiplied by 100; enrolment in katchi is excluded.

MDGs goal 3 aims to eliminate gender disparity in primary and
secondary preferably by 2005 and to all levels of education no latter
than 2015. To measure progress in this goal the study takes the ratio of
girls to boys in completed primary level or higher: number of girls per
100 boys [United Nation (2002)].

(ii) Health

Goal 4: Reduced Child Mortality

This goal targets a reduction in child mortality by two third
between 1990 and 2015 (reduction in infant mortality rate to 52 and
child mortality rate to 77). Progress in this goal is measured through
an indicator: proportion of fully immunised children 12-23 months old.
The Pakistan Expanded Programme on Immunisation (EPI) follows the
international guidelines recommended by the World Health Organisation
(WHO). The guidelines recommended for all children a BCG vaccination
against tuberculosis; three doses of DPT vaccine for the prevention of
diphtheria, pertussis (whooping cough) and tetanus; three doses of polio
vaccine and a vaccination against measles during the first year of the
child's life. Progress in child health is measured through recall
and record of full immunisation course which means that the children age
12-23 months had received: BCG, DPT1, 2, 3, Poliol, 2, 3 and measles
[United Nation (2002)].

Goal 5: Improve Maternal Health

This goal aims to reduce maternal mortality rate by three quarters
between the 1990-2015 periods that is 140 per 100,000 lives births.
Efforts to reduce maternal mortality need to be tailored to local
conditions, since the causes of death vary across developing regions and
countries. The over all maternal mortality ratio is at 276 maternal
deaths per 100,000 live births and approximately 1 in 89 women in
Pakistan will die of maternal causes during her life time taken as
lifetime risk [NIPS (2008)]. The success of this goal is measured
through these indicators; prenatal consultation measured as woman
received at least one Tetanus Toxoid injection, safe delivery is taken
as health personals that assisted in delivery (doctor, nurse, midwives),
location of delivery is considered as child birth taken place at
government or private health units and post natal consultations is
measured as received medical check up within six weeks of delivery for
women aged 15-49 years who had a birth in the last three years.

(iii) Living Conditions

Goal 7: Ensure Environmental Sustainability

A household's access to civic amenities is determined not only
by its location but also by its economic circumstances. Thus access to
such services can vary across households from different districts
because no district provides universal coverage. In Pakistan for the
measurement of environmental sustainability four indicators are adopted;
proportion of population with sustainable access to an improved water
source (tap water, motor pump and hand pump) and proportion of people
with access to improved sanitation ('flush' consists of flush
connected to public sewerage/septic tank / open drain) which are
included in MDGs indicators [United Nation (2002)]. Two more indicators
are also taken to ensure environmental sustainability, i.e. source of
lighting measured as percentage of households have electricity
connections and percentage of households using gas or kerosene oil as
fuel used for cooking.

3.6. Dimensions of Subjective Well-being Index (SWBI)

By dimension mean "any of the component aspects of a
particular situation". The key features of dimensions of subjective
well-being are based on people's perceptions of their quality of
life and satisfaction with living conditions. These indicators are
survey based and directly enquire individuals about their satisfaction
with the services/facilities available to them. Subjective measurement
involves self reports based on implicit criteria.

Subjective Indicators

To estimate human well-being objective indicators be supplemented
by subjective ones, as proposed by [Veenhoven (2007) and Hasan (2008)]
since both capture different dimensions of well-being. The formula for
the overall index of subjective well-being is as follows:

To measure subjective well-being, indicators are taken which are
based on use and satisfaction with the facilities, expressed as
percentage of those households who used these services. (2) This type of
information has been collected for the first time in FBS household
surveys. Since government is spending lot to improve the economic
situation of people and also investing considerable amount in providing
different types of facilities and services. Considering as how
facilities / services are being passed on to the general public, the
respondents are asked to give their perception in their economic as well
as community improvement and how effectively services are available to
them. To measure subjective well-being education, health and security
measured by police services, households are asked to give opinion about
their satisfaction of the facilities/services provided by the
government.

3.7. Standard Scores for Categorisation of Well-being Index (WBI)

It indicates where the score lies in comparison to mean i.e. if the
mean of index is [X.sub.w], then the score can be compared to see if it
is above or below this average. Standard deviation (SD) around the mean
(both side plus and minus) is taken to categorisation of the
distribution of well-being index; where, w = 1, 2 (objective index and
subjective index, simultaneously). Following [Li, et al. (1998) and
Cummins (2000)], the six categories are classified as:

This technique is also used to observe the sensitiveness of the
results with respect to the choice of technique for deriving the
composite indicators. The [Z.sub.sum] is the standardised score, which
has zero mean and unit variance. The higher the [Z.sub.-sum] the more
developed the district.

4. ANALYSIS

Classifying the districts in terms of categories of objective index
value, i.e., highest, higher, upper medium, lower medium, low and lowest
provides a useful basis for the analysis. For ease of comparison,
absolute values of variables are standardised to a scale of 0 to 1 by
using Linear Scaling Technique (LST) which linearly scales variables to
a uniform range. It also assigns the lowest implicit weights to
variables and deals with the directionality issue and provides a
consistent way to aggregate variables. The composite index value gives
the achievement in the level of well-being; the higher the value of
index the more the level of well-being. The findings of this analysis
indicate that average index value of 100 districts is 49.02 percent
whereas average achievement is 74.9 percent for 17 districts in highest
category while the average value of the lowest well-being index is 21.75
percent. Table 3.a gives information regarding the ranking of districts
in term of highest and high well-being. Karachi, Rawalpindi and Lahore
etc, are ranked in highest category among 17 districts with average 74.9
percent achievements in its dimensions with overall 37.37 percent share
in population (Table 4). Second category is high well-being which
includes 14 districts with overall population share is 16.48 percent.
Multan, Sahiwal and Nowshera are ranked top approximately with average
achievement of 63.65 percent. It is important to note that three out of
four provincial capitals, i.e., Karachi, Lahore and Quetta are ranked in
highest category while Peshawar comes at 29 in district ranking of
well-being. The dominance of Punjab is observed in highest well-being
category where thirteen out of seventeen districts belong to this
province, like Rawalpindi, Lahore, Gujrat, Gujranwala, Sialkot, Jehlum,
Toba Tek Singh, Faisalabad etc. In second category of high well-being
only districts of Punjab and NWFP are emerged. This tends to indicate
that Punjab is ahead of the other provinces in terms of objective
indicators. The relatively high enrolment rates at primary level along
with access to maternal health care services are the prime reason for
the relatively high ranking of districts in this province [Pakistan
(2008)]. Ghaus, et al. (1996) ranked districts in terms of social
development using Z_sum and weighted factor scores also come to same
conclusion as in the present analysis.

Table 3b classifies districts with upper medium and lower medium
level of well-being. The upper medium category has 19 districts with
average achievement of 54.51 percent with population share of 22.9
percent. Khanewal, Nowshero Feroz and Mardan are ranked top in this
classification. Districts of Punjab again dominates this category where
ten out of 19 districts are from this province, Sindh and NWFP have 3
and 5 districts respectively while only one district is from
Balochistan. One can draw the conclusion that if a district starts with
an advantage in human endowment, it is easier to maintain its relative
position [Pasha and Naeem (1999)]. The fourth category of well-being is
lower medium with average index value is 43.48 percent which is less
than overall average value of well-being index. Sindh and NWFP districts
are dominated in this category.

The last two categories which consist of 31 districts are dominated
by Balochistan, with 19 districts belonging to this province followed by
NWFP and Sindh as presented in Table 3c. By and large, the differences
in health and educational outcomes between districts reflect the
differences in access to these services. The rank ordering of districts
indicates that gender disparity in education and lack of maternal health
care services dominates the outcome. Analysis of the magnitude of
indicators in the relatively underdeveloped districts indicates that the
profile of backwardness is primarily of poor quality of civic immunities
with low access to water, sanitation, electricity and gas and also with
low standards of provision of health and education facilities.

The ranking exercises help in identifying the districts having the
greatest need for intervention to achieve the MDGs targets. It can be
used in the process of policy making and planning, decision-making
regarding resource allocation and selection of districts for
intervention programmes, and monitoring and evaluation at the district
level.

[FIGURE 1 OMITTED]

Figure 1 plots the relative position of districts across four
provinces of Pakistan where the name of districts are labeled in
alternative manner. Karachi ranks at the top while Dera Bugti is placed
at the lower end.

A look at Table 4 shows disparities in terms of percentage share of
population in objective well-being categories across provinces. It is
observed that Punjab has highest share of population in top category of
well-being while population of Balochistan gets major share in lowest
category.

To estimates the quality of life in Pakistan, [Veenhoven (2007) and
[Hasan (2008)] recommended that objective indicators be supplemented by
subjective ones, since both capture different dimensions of well-being.
Subjective indicators focus on soft matters such as satisfaction with
income and measures individual perceptions based on a respondent's
judgment rather than that of policy-makers or researchers while
objective indicators measures hard facts. The following tables rank
districts of Pakistan in three categories which further splits into six
classifications. To measure subjective well-being of households,
indicators are taken which are based on use and satisfaction with the
facilities, expressed as percentage of those households who used these
services i.e., education, health and security measured by police
services. It is interesting to note that ranking on the bases of
subjective well-being is entirely different from objective well-being as
highest districts are not appeared at the top ranked in subjective
well-being index.

It is important to note here that subjective view of utility
recognises that everybody has his or her own ideas about happiness and
the quality of life that observed behaviour is an incomplete indicator
for individual. People evaluate their level of subjective well-being
with regard to circumstances and comparison to other person, past
experiences and expectation of the future. Measure of subjective
well-being can thus serve as proxies for 'utility' since its
item are subject to the law of diminishing utility [Veenhoven (2007)].

Keeping in view of above discussion, subjective well-being in
hundred districts of Pakistan is estimated. Out of which 16 districts
lie in first category of highest well-being, where Swat, Vehari and
Nowshero Feroz ranks at the top while in second category of high
well-being Lakki Marwat, Dera Ismail Khan and Layyah comes first as
presented in Table 5a, although Ghaus, et al. (1996) indicated that
these districts are least developed in terms of social development
related to education, health and water supply.

Tables 5b and 5c ranks other two categories of subjective
well-being in districts of Pakistan. Three provincial capitals, Quetta,
Karachi and Lahore which are classified in top ranking of objective
well-being are now ranked in second and third category of subjective
well-being. Most of the less developed districts of Balochistan
invariably have not changed their position in these two well-being
indices i.e., objective and subjective well-being. Here the important
role of hard facts of well-being is not denied or minimised, because not
only people living in developed regions score higher in the measurement
of their satisfaction index but also when poor people receive even a
modest increase in their facilities, their satisfaction level grows.
Nevertheless, for less developed regions, the modest increase is merely
a temporary phenomenon because such a nominal increase might simply
fulfil their basic human needs and not their desires.

A look at Table 6 shows disparities in terms of percentage share of
population in subjective well-being categories across provinces. It is
observed that Sindh has highest share of population in top category of
well-being while perception of Punjab population is lowest in this
category. This indicates that people of Punjab are least satisfied with
exiting facilities available to them in terms of education, health and
security while people of Sindh are happier with services available to
them. Several authors argue that subjective satisfaction is affected by
comparisons between one's own situation and that of his or her
peers.

Figure 2 plots index of subjective well-being where the ranking are
labeled in alternative districts. District Swat ranks at the top while
Qilla Safullaha is placed at the lower end.

[FIGURE 2 OMITTED]

It is argued that social policy still needs subjective indicators
and those objective indicators taken alone are inadequate. It is
commonly objected that matter of the mind are unstable, incomparable and
unintelligible and the subjective appraisals cannot be compared between
persons. One assertion is that different people use different criteria,
so two persons stating they are very happy can say so for different
reasons. Another claim is that people have different scales in mind, and
that people who report they are 'very happy' may in fact be
equally as happy as someone who characterises his life as 'fairly
happy'. Likewise it is argued that subjective appraisals can not be
compared across culture as notion of poverty differ greatly between rich
and poor nations and within nations between upper and lower classes
which means for social policy these kinds of indicators tell policy
makers little about relative performance. A related objection is that
the criteria used for these subjective appraisals are largely implicit.
In spite of these weaknesses, subjective indicators are indispensable in
social policy, both for assessing policy success and for selecting
policy goals. Achieving some goals or targets of MDGs, different
dimensions of well-being should be taken into account as objective
measures have limited validity and reliability. Joint use of objective
and subjective measures is mostly helpful to get a complete picture,
while rigid restriction to objective indicators considerably narrows the
perspective [Veenhoven (2007)]. Since the underlying premise of the MDGs
is still the concept of human development, so main streaming of
subnational or local targets into the national targets and priorities is
needed to concentrate on least developed districts for achieving the
MDGs by 2015. These can be achieved if immediate steps are taken to
implement existing commitments. Reaching the goals for development in
each district of Pakistan is vital to building better, healthier and
decent lives for millions of people in the country. Least developed
districts within each province are identified as targets for special
development allocations with Medium Term Development Framework (MTDF).

Table 7 presents a matrix of objective well-being and subjective
well-being differences as developed by Veenhoven (2002) which is
constructed by taking into account the major three classification of
well-being [Tables 3a, b, c and Tables 4a,b,c]. The districts which are
placed at diagonal, objective and subjective well-being coincide. It is
interesting to note that all the provincial capitals are placed in high
objective well-being index but the perception towards satisfaction of
available services is low except NWFP provincial capital, Peshawar. Most
of the districts of Balochistan with least developed social indicators
are in low category in respect of these two well-being indices.
Information about perception and satisfactions of households is quite
useful in the policy process and the degree to which long and happy life
is an important criterion for final policy effectiveness of MDGs. To
meet MDGs targets by 2015, Pakistan will have to achieve GDP growth rate
of 7-8 percent per annum, ensure continuity and sustainability of
reforms, allocate additional resources and ensure their effective use,
and above all increasingly involve communities in the development
process [Pakistan (2008)].

In Appendix Tables 1 to 4, findings from Z-sum technique are also
presented to observe the robustness of the results with respect to the
choice of technique for deriving the composite indicators. The analysis
shows the validity of well-being measures by indicating convergence in
both well-being measures as there are no important discrepancies in
districts ranking which generalised that there are no major unobserved
variations in well-being indices.

How to Explain Districts Disparities in Well-being?

The real question is how to explain districts disparities in
well-being in Pakistan. In other words why is quality of life
considerably lower in one area than in other areas? Some explanations in
terms of socio-economic development indicators are also given as:

(1) Remittances from overseas migrants, especially from Middle East
play an important role in quality of life for Pakistani people. Recent
data shows that sixty percent Pakistani in the Middle East migrated from
only 20 districts with heavy concentration from Karachi, Rawalpindi,
Lahore, Swat, Faisalabad, Gujranwala, etc.

(2) Incidence of poverty is low in high well-being districts while
it is quite high in 'low' or 'lowest' objective
well-being districts. Per capita expenditure is quite high in
'good' and 'fair' rated objective well-being
districts as compared to 'poor' or 'bad' rated
quality of life [Cheema, et al. (2008)].

(4) High dependency of the rural labour force on the agriculture
sector in poor districts is seen.

(5) Districts which have industrial zone i.e., Karachi, Lahore,
Faisalabad, Gujranwala, etc are in high well-being.

(6) Large family size, high dependency ratio in poor districts is
observed in the Population Census of Pakistan, 1998.

(7) Inequality in ownership of land is observed in Pakistan; only
less than half of all rural households own any agriculture land while
the top 2.5 percent of all households account for over 40 percent of all
land owned. Gini coefficient for land distribution is high in
'poor' or 'bad' rated objective well-being
districts. [Amjad, et al. (2008)].

6. CONCLUSIONS

The concern for measuring well-being objectively and subjectively
is found in modern political philosophy. This paper attempts to
implement empirically some of the multidimensional concepts of human
well-being. Using data from the 'Pakistan Social and Living
Standards Measurement Survey' 2006-07, objective well-being index
and subjective well-being index are constructed. In the objective
well-being approach the focus is on measuring 'hard' facts
such as living conditions while subjective well-being approach in
contrast consider 'soft' matters such as satisfaction with
available facilities. Non-monetary human development indicator i.e.
education, health and living conditions are taken in the context of
Millennium Development Goals to analyse the level of well-being across
districts of Pakistan. The indices are classified in three categories,
high, medium and low each with two sub categories.

The findings of the study indicate variation in the indicators of
well-being across the districts of Pakistan which is an indicative of
regional disparities in the quality of life. The composite index value
gives the achievement in the level of well-being; the higher the value
of index the more the level of well-being. Karachi, Rawalpindi, Lahore,
Gujrat, Gujranwala, Sialkot, Jehlum, Chakwal, T.T.Singh and Faisalabad,
etc. are ranked in highest objective well-being category among 17
districts which accounts for 37 percent share of country population.
Federal and all the provincial capitals are ranked as, Islamabad,
Karachi, Lahore, Quetta and Peshawar in high well-being category. It may
be noted that most of the top ranked districts are located in the
provinces of Punjab which tends to indicate that Punjab is ahead of
other provinces in terms of objective well-being. Sindh and NWFP
districts are dominated in the category of lower medium well-being. At
the lower end of the distribution districts of Balochistan emerged in
lowest well-being category. It is observed that Punjab have highest
share of population in top category of well-being (67.8 percent) while
population of Balochistan gets major share in bottom well-being category
(73 percent). It is interesting to note that ranking on the bases of
subjective well-being is entirely different from objective well-being as
highest objective well-being districts are appeared in medium and low
subjective well-being categories. It means the higher the achievements
in hard facts of well-being the less satisfaction in terms of
services/facilities they used. But most of the districts of Balochistan,
with least developed well-being indicators, perception about the quality
of life is evident. Since, subjective appraisals can not be compared
across culture as concept of well-being differ greatly between rich and
poor within nations between upper and lower classes which means for
social policy these kinds of indicators tell policy makers little about
relative performance. In spite of these weaknesses, subjective
indicators are indispensable in social policy, both for assessing policy
success and for selecting policy goals. However, the results indicate
substantial variation among districts within a province in the level of
well-being.

Since the underlying premise of the MDGs is still the concept of
human development, so main streaming of sub-national or local targets
into the national targets and priorities is needed to concentrate on
least developed districts for achieving the MDGs by 2015. These can be
achieved if immediate steps are taken to implement existing commitments.
Reaching the goals for development in each district of Pakistan is not
only vital for building better, healthier and decent lives for millions
of people in the country. Least developed districts within each province
are identified as targets for special development allocations with MTDF.

REFERENCES

Akhtar, S. and M. N. Sarwer (2007) Social Development and Quality
of Living in Districts of Pakistan. Comparative Ranking between 1998 and
2004-05. Centre for Research on Poverty Reduction and Income
Distribution. (Discussion Paper Series No. 16).

Siddiqui, R. (2006) The Role of Household Income and Public
Provision of Social Services in Satisfaction of Basic Needs in Pakistan:
A Cross District Analysis. Presented in 22nd Annual General Meeting and
Conference of the Pakistan Society of Development Economists December
19-21, 2006, Lahore.

Uddin, F. (2007) State of Social Development in Pakistan: Issues of
Access and Quality. Pakistan Economy: An Assessment with Special
Reference to Quality of Life. Institute of Policy Studies, Islamabad.

United Nation (2002) Implementation of the United Nations
Millennium Declaration. Report of the Secretary-General. United Nation.

Authors' Note: The authors are indebted to Dr Attiya Javid and
Lubna Hasan for their comments and suggestions on this paper.

Rashida Haq and Uzma Zia
are Senior Research Economist and Staff
Economist, respectively at the Pakistan Institute of Development
Economics, Islamabad.

(1) Equally weighted indices are used frequently in the literature
of well-being for example UNDP's Human Development Index and
International Development Research Centre's (IDRC) Human Well-being
Index.

(2) The non-marketed services such as education, health and
sanitation etc., are used as evaluative criteria in subjective
well-being [Kingdon and John (2005)].

(3) Population shares are based on 'Pakistan Population and
Housing Census (1998)'; although absolute number of districts
population has increased during 1998 to 2006-07 but there is less
significant change in proportional share of districts population.